VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity

Summary: VEGA uses active-tuning with group-wise granularity to simplify distribution and tighten lookup bounds. A memory-budget framework merges key grouping with online key repositioning to achieve strong theory and empirical lookup/build performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h6b9230cd96c4dcc4
Venue: SIGMOD
Year: 2025
Pagerank: 4.9793485e-05
Overall Rank: 11,122 | 25.23%
DOI: 10.1145/3709736

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Meng Li (Nanjing University)
2. Huayi Chai (Nanjing University)
3. Siqiang Luo (Nanyang Technological University)
4. Haipeng Dai (Nanjing University)
5. Rong Gu (Nanjing University)
6. Jiaqi Zheng (Nanjing University)
7. Guihai Chen (Nanjing University)

BibTeX Citation

@inproceedings{li_sigmod25,
        title = {{VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity}},
        author = {Li, Meng and Chai, Huayi and Luo, Siqiang and Dai, Haipeng and Gu, Rong and Zheng, Jiaqi and Chen, Guihai},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3709736},
        url = {https://dl.acm.org/doi/10.1145/3709736},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,643	Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding	2026	SIGMOD	5.1453267e-05

Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046284649
69	Making B+-Trees Cache Conscious in Main Memory	2000	SIGMOD	0.00037950385
207	Cache Conscious Indexing for Decision-Support in Main Memory	1999	VLDB	0.00024970987
229	A Study of Index Structures for Main Memory Database Management Systems	1986	VLDB	0.00023911856
282	FAST: Fast Architecture Sensitive Tree Search on Modern CPUs and GPUs	2010	SIGMOD	0.00022264207
430	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018409112
463	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00017804544
548	Improving Index Performance through Prefetching	2001	SIGMOD	0.00016569534
779	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00014030069
848	Benchmarking Learned Indexes	2021	VLDB	0.00013506188
971	Reducing the Storage Overhead of Main-Memory OLTP Databases with Hybrid Indexes	2016	SIGMOD	0.00012766019
1,550	Updatable Learned Index with Precise Positions	2021	VLDB	0.00010282449
2,277	FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems	2022	VLDB	8.7075835e-05
2,636	Are Updatable Learned Indexes Ready?	2022	VLDB	8.1941043e-05
3,206	CDFShop: Exploring and Optimizing Learned Index Structures	2020	SIGMOD	7.5397402e-05
3,803	CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm	2022	VLDB	7.0127652e-05
4,375	DILI: A Distribution-Driven Learned Index	2023	VLDB	6.6292167e-05
4,508	NFL: Robust Learned Index via Distribution Transformation	2022	VLDB	6.5704964e-05
4,616	The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures	2022	SIGMOD	6.5000712e-05
5,537	Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices	2023	SIGMOD	6.0919359e-05
5,571	FILM: a Fully Learned Index for Larger-than-Memory Databases	2023	VLDB	6.080267e-05
6,274	A Critical Analysis of Recursive Model Indexes	2022	VLDB	5.8271888e-05
6,821	Making In-Memory Learned Indexes Efficient on Disk	2024	SIGMOD	5.6720444e-05
6,863	Accelerating String-key Learned Index Structures via Memoization-based Incremental Training	2024	VLDB	5.6607844e-05
7,018	SALI: A Scalable Adaptive Learned Index Framework based on Probability Models	2023	SIGMOD	5.619958e-05

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